Audio Occupancy Detection Using Feature Extraction and Classification
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Solution Overview
Problem
Conventional occupancy detection systems face challenges in accurately determining presence and head count in environments with background noise, limited detection distance, and privacy concerns, often requiring multiple sensors and increased energy consumption.
Innovation Solution
An audio-based occupancy detection system that uses a processor to extract low-level descriptive features from audio samples, selects relevant features using statistical operations, and employs a decision tree classifier to determine occupancy, reducing the need for multiple sensors and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple audio sensors are used to accurately determine presence or head count, then detection accuracy is improved, but energy consumption and computational complexity increase
Solution Approach 1:
The patent combines multiple audio sensors into a single virtual sensor by aggregating their signals through feature extraction and statistical operations. The system processes audio features from multiple sensors and uses a classifier to determine occupancy, achieving accurate detection while reducing the physical number of sensors and associated energy consumption.
Solution Approach 2:
The audio processing system performs multiple functions using a unified approach: it extracts features, applies statistical operations, and uses a classifier to determine both presence and head count. This multi-functional processing reduces the need for separate specialized sensors for each detection task.
2Measurement precision
If multiple audio sensors are used to accurately determine presence or head count, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent extracts key audio features (spectral characteristics, temporal patterns, etc.) from the audio signals and separates them from the raw data. By working with these extracted features rather than the complete audio streams, the system reduces computational complexity while maintaining detection accuracy.
Solution Approach 2:
The system divides the audio processing into distinct segments: feature extraction, statistical operations, and classification. This segmentation allows each component to be optimized independently and reduces overall computational burden by processing only necessary portions of the audio data at any given time.
3Measurement precision
If video sensors are used for occupancy detection, then detection capability is improved, but privacy concerns increase
Solution Approach 1:
The patent replaces video-based detection with audio-based detection. Instead of using cameras to visually observe occupants (which raises privacy concerns), the system uses microphones to capture audio signals and analyze acoustic features to determine occupancy. This substitution maintains detection capability while eliminating the privacy intrusion associated with video monitoring.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively detects occupancy and head count with reduced computational resources and energy consumption, providing accurate results in various environments while being less intrusive than video-based systems.
Implementation Method 1
obtaining, with a processor, an audio sample from an audio sensor
Data Source
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AI summary
A method of detecting occupancy in an area includes obtaining, with a processor, an audio sample from an audio sensor and determining, with the processor, feature functional values of a set of selected feature functionals from the audio sample. The determining of the feature functional values includes extracting features in the set of selected feature functionals from the audio sample, and determining the feature functional values of the set of selected features from the extracted features. The method further includes determining, with the processor, occupancy in the area using a classifier based on the determined feature functional values.